Data Science Projects with Python
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Data Science Projects with Python
By:
Overview of this book
Data Science Projects with Python is designed to give you practical guidance on industry-standard data analysis and machine learning tools, by applying them to realistic data problems. You will learn how to use pandas and Matplotlib to critically examine datasets with summary statistics and graphs, and extract the insights you seek to derive. You will build your knowledge as you prepare data using the scikit-learn package and feed it to machine learning algorithms such as regularized logistic regression and random forest. You’ll discover how to tune algorithms to provide the most accurate predictions on new and unseen data. As you progress, you’ll gain insights into the working and output of these algorithms, building your understanding of both the predictive capabilities of the models and why they make these predictions.
By then end of this book, you will have the necessary skills to confidently use machine learning algorithms to perform detailed data analysis and extract meaningful insights from unstructured data.
Table of Contents (9 chapters)
Data Science Projects with Python
Preface
Free Chapter
Data Exploration and Cleaning
Introduction toScikit-Learn and Model Evaluation
Details of Logistic Regression and Feature Exploration
The Bias-Variance Trade-off
Decision Trees and Random Forests
Imputation of Missing Data, Financial Analysis, and Delivery to Client
Appendix
Customer Reviews